GNN-Constrained Beam Search for Structured Question Response
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Solution Overview
Problem
Existing customer grievance redressal systems face challenges in providing prompt and relevant responses to customer queries due to the limitations of pre-trained language models and the need for improved integration of knowledge graphs to enhance response relevance and structure.
Innovation Solution
Integrating pre-trained language models with knowledge graphs using graph neural networks (GNNs) and reinforcement learning to generate structured responses, ensuring joint reasoning across both modalities through interaction tokens and nodes, and utilizing beam search for optimized prompt generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-trained language models are used for customer query responses, then response generation speed is improved, but response relevance and structure deteriorate
Solution Approach 1:
The patent merges pre-trained language models with knowledge graphs and graph neural networks into a hybrid system. The language model provides fast response generation while the knowledge graph contributes structured domain knowledge and relationships, and the GNN processes the graph data to generate structured responses. This combination resolves the contradiction by integrating the speed advantage of language models with the relevance and structure advantages of knowledge graphs.
Solution Approach 2:
The patent introduces graph neural networks as an intermediary component that processes information from knowledge graphs and transforms it into structured response formats. The GNN acts as a mediator between the knowledge graph's structured data and the language model's generative capabilities, enabling the system to maintain both speed and structural precision in responses.
2Manufacturing precision
If knowledge graphs are integrated to enhance response structure, then response relevance is improved, but system complexity increases
Solution Approach 1:
The patent designs the graph neural network to perform multiple functions: processing knowledge graph data, generating structured responses, and working协同 with the language model. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while achieving improved response structure and relevance.
Solution Approach 2:
The patent embeds the knowledge graph processing within the language model framework. The GNN processes knowledge graph information and outputs structured representations that are then fed to the language model for final response generation. This nested architecture allows the complex knowledge graph processing to be contained within the existing language model infrastructure, reducing overall system complexity.
3Measurement precision
If beam search is used for optimized prompt generation, then response accuracy is improved, but computation time increases
Solution Approach 1:
The patent applies beam search with a controlled beam width that balances exploration and computation time. Instead of exhaustively searching all possible prompts (which would be excessive action), the beam search maintains a manageable number of candidate prompts (partial action) that are sufficient to achieve high accuracy while controlling computation time through parameter tuning.
Data Source
AI summary
A method for updating a question-answer mechanism includes obtaining, by a data system implementing the question-response mechanism, an input query, in response to the input query: generating a token sequence associated with the input query by applying a beam search algorithm on generated bound knowledge graphs, applying the input query to a large language model (LLM) of the question-response mechanism to obtain first outputs, applying the generated token sequence to a graph neural network (GNN) to obtain second outputs, applying fusion layers on the first outputs and the second outputs to generate a response associated with the input query and generated token sequence, and performing a remediation using the generated response, wherein the remediation comprises updating the LLM and the GNN based on the generated response.